2.4 SFARI Genes

Load R packages

library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally) ; library(ggpubr)
library(rstatix)
library(expss)
library(DESeq2)
library(knitr) ; library(kableExtra)

Load preprocessed dataset (code in 2.1.Preprocessing_pipeline.Rmd)

# SFARI Genes
SFARI_genes = read_csv('./../../SFARI/Data/SFARI_genes_01-03-2020_w_ensembl_IDs.csv')

# Load Gandal dataset
load('./../Data/preprocessedData/preprocessed_data.RData')
datExpr = datExpr %>% data.frame

# Updates genes_info with SFARI information
genes_info = genes_info %>% left_join(SFARI_genes, by = 'ID') %>%
             mutate(gene.score = ifelse(is.na(`gene-score`) & Neuronal==0, 'Others', 
                                        ifelse(is.na(`gene-score`), 'Neuronal', `gene-score`))) %>%
             mutate(Group = factor(ifelse(gene.score %in% c('Neuronal','Others'), gene.score, 'SFARI'), 
                    levels = c('SFARI', 'Neuronal', 'Others')))

# SFARI palette
SFARI_colour_hue = function(r) {
  pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}

---

There are 864 SFARI Genes in the expression dataset (~77%), 789 of them have a SFARI Gene Score.

Gene count by SFARI score:

table_info = genes_info %>% apply_labels(`gene-score` = 'SFARI Gene Score', syndromic = 'Syndromic Tag',
                                          Neuronal = 'Neuronal Function', gene.score = 'Gene Score') %>%
             mutate(syndromic = as.logical(syndromic), Neuronal = as.logical(Neuronal))

cro(table_info$`gene-score`)

	#Total
SFARI Gene Score
1	144
2	205
3	440
#Total cases	789

Gene count by Syndromic tag:

cro(table_info$syndromic)

	#Total
Syndromic Tag
FALSE	748
TRUE	116
#Total cases	864

Neuronal annotations:

1545 genes have neuronal-related annotations, 201 of these, have a SFARI score

cro(table_info$gene.score[genes_info$`gene-score` %in% as.character(c(1:3))],
    list(table_info$Neuronal[genes_info$`gene-score` %in% as.character(c(1:3))], total()))

	Neuronal Function			#Total
	FALSE	TRUE
Gene Score
1	97	47		144
2	146	59		205
3	345	95		440
#Total cases	588	201		789

rm(table_info)

---

2.4.1 Analysis of all SFARI Genes together

Mean Expression

plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>% 
            left_join(genes_info, by='ID')

wt = plot_data %>% wilcox_test(MeanExpr~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1
base = 15.7
pos_y_comparisons = c(base, base+increase, base)
plot_data %>% ggplot(aes(Group, MeanExpr)) + 
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) + 
     stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
     scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) + 
     xlab('') + ylab('Mean Expression') + #ggtitle('Mean Expression Comparison') +
     scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
                              'Neuronal' = 'Neuronal\ngenes')) +
     theme_minimal() + theme(legend.position='none')

Log Fold Change

The propotion of over and underexpressed genes is very simiar for each group, so we can just focus on the magnitude of the LFC independently of the sign.

genes_info %>% mutate(direction = ifelse(log2FoldChange>0, 'overexpressed', 'underexpressed')) %>% 
               group_by(Group, direction) %>% tally(name = 'overexpressed') %>% 
               filter(direction == 'overexpressed') %>% ungroup %>% 
               left_join(genes_info %>% group_by(Group) %>% tally(name = 'Total'), by = 'Group') %>% ungroup %>%
               mutate('underexpressed' = Total - overexpressed , 
                      'ratio' = round(overexpressed/underexpressed,2)) %>% 
               dplyr::select(Group, overexpressed, underexpressed, ratio) %>% 
               kable %>% kable_styling(full_width = F)

Group	overexpressed	underexpressed	ratio
SFARI	379	410	0.92
Neuronal	673	671	1.00
Others	7194	6790	1.06

plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>% 
            left_join(genes_info, by='ID')

wt = plot_data %>% filter(log2FoldChange>0) %>% 
     wilcox_test(MeanExpr~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1
base = 15.7
pos_y_comparisons = c(base, base+increase, base)
p1 = plot_data %>% filter(log2FoldChange>0) %>% ggplot(aes(Group, MeanExpr)) +
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) + 
     stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
     scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) + 
     xlab('') + ylab('Mean Expression') + ggtitle('Overexpressed genes') +
     scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
                              'Neuronal' = 'Neuronal\ngenes')) +
     theme_minimal() + theme(legend.position='none')

wt = plot_data %>% filter(log2FoldChange<0) %>% 
     wilcox_test(MeanExpr~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
p2 = plot_data %>% filter(log2FoldChange<0) %>% ggplot(aes(Group, MeanExpr)) +
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) + 
     stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
     scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) + 
     xlab('') + ylab('Mean Expression') + ggtitle('Underexpressed genes') +
     scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
                              'Neuronal' = 'Neuronal\ngenes')) +
     theme_minimal() + theme(legend.position='none')

grid.arrange(p1, p2, nrow=1)

Both with the original LFC values as well as the shrunken ones, the difference in LFC magnitude between SFARI genes and Neuronal genes is statisically significant with a p-value lower than \(10^{-4}\).

wt = genes_info %>% mutate(abs_lfc = abs(log2FoldChange), 
                           Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>% 
     wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')

increase = 0.04
base = 0.45
pos_y_comparisons = c(base, base + increase, base)
p1 = genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>% 
     ggplot(aes(Group, abs(log2FoldChange))) + 
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) + 
     stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .005) + 
     scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) + 
     coord_cartesian(ylim= c(0, max(pos_y_comparisons))) + 
     scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
                              'Neuronal' = 'Neuronal\ngenes')) +
     xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none')


wt = genes_info %>% mutate(abs_lfc = abs(shrunken_log2FoldChange), 
                           Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>% 
     wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')

increase = 0.03
base = 0.35
pos_y_comparisons = c(base, base + increase, base)
p2 = genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>% 
     ggplot(aes(Group, abs(shrunken_log2FoldChange))) + 
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) + 
     stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .01) + 
     scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) + 
     coord_cartesian(ylim= c(0, max(pos_y_comparisons))) + 
     scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
                              'Neuronal' = 'Neuronal\ngenes')) +
     xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none')

grid.arrange(p1, p2, nrow = 1)

rm(increase, base, pos_y_comparisons, wt)

Full plots, without cropping out outliers

p1 = ggplotly(genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>% 
     ggplot(aes(Group, abs(log2FoldChange))) + 
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) + 
     scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) + 
     scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
                              'Neuronal' = 'Neuronal\ngenes')) +
     xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none'))
  
p2 = ggplotly(genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>% 
     ggplot(aes(Group, abs(shrunken_log2FoldChange))) + 
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) + 
     scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) + 
     scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
                              'Neuronal' = 'Neuronal\ngenes')) +
     xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none'))
  

subplot(p1, p2, nrows = 1)

Differential Expression

genes_info %>% group_by(Group, significant) %>% tally(name = 'DE') %>% filter(significant) %>% ungroup %>%
               left_join(genes_info %>% group_by(Group) %>% tally(name = 'Total'), by = 'Group') %>% ungroup %>%
               mutate('prop_DE' = round(DE/Total,2)) %>% dplyr::select(Group, DE, prop_DE, Total) %>% 
               kable(caption = 'Proportion of DE Genes by Group') %>% kable_styling(full_width = F)

Proportion of DE Genes by Group

Group	DE	prop_DE	Total
SFARI	242	0.31	789
Neuronal	482	0.36	1344
Others	3749	0.27	13984

lfc_list = seq(0, 0.3, 0.01)

all_counts = data.frame('group'='All', 'n'=as.character(nrow(genes_info)))
Others_counts = data.frame('group'='Others', n=as.character(sum(genes_info$Group == 'Others')))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(genes_info$Group == 'Neuronal')))
lfc_counts_all = genes_info %>% filter(Group == 'SFARI') %>% tally %>%
                 mutate('group'='SFARI', 'n'=as.character(n)) %>% 
                 dplyr::select(group, n) %>%
                 bind_rows(Neuronal_counts, Others_counts, all_counts) %>%
                 mutate('lfc'=-1) %>% dplyr::select(lfc, group, n)

for(lfc in lfc_list){
  
  # Recalculate genes_info with the new threshold (p-values change)
  DE_genes = results(dds, lfcThreshold=lfc, altHypothesis='greaterAbs') %>% data.frame %>% 
             mutate('ID' = rownames(.)) %>% 
             left_join(genes_info %>% dplyr::select(ID, Neuronal, gene.score, Group), by = 'ID') %>% 
             filter(padj<0.05 & abs(log2FoldChange)>lfc)

  
  # Calculate counts by groups
  all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_genes)))
  Others_counts = data.frame('group'='Others', n=as.character(sum(DE_genes$Group == 'Others')))
  Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_genes$Group == 'Neuronal')))
  lfc_counts = DE_genes %>% filter(Group == 'SFARI') %>% tally %>%
               mutate('group'='SFARI', 'n'=as.character(n)) %>%
               bind_rows(Neuronal_counts, Others_counts, all_counts) %>%
               mutate('lfc'=lfc) %>% dplyr::select(lfc, group, n)
  
  
  # Update lfc_counts_all
  lfc_counts_all = lfc_counts_all %>% bind_rows(lfc_counts)
}

# Add missing entries with 0s
lfc_counts_all = expand.grid('group'=unique(lfc_counts_all$group), 'lfc'=unique(lfc_counts_all$lfc)) %>% 
  left_join(lfc_counts_all, by=c('group','lfc')) %>% replace(is.na(.), 0)

# Calculate percentage of each group remaining
tot_counts = genes_info %>% filter(Group == 'SFARI') %>% tally() %>%
             mutate('group'='SFARI', 'tot'=n) %>% dplyr::select(group, tot) %>%
             bind_rows(data.frame('group'='Neuronal', 'tot'=sum(genes_info$Group == 'Neuronal')),
                       data.frame('group' = 'Others', 'tot' = sum(genes_info$Group == 'Others')),
                       data.frame('group'='All', 'tot'=nrow(genes_info)))

lfc_counts_all = lfc_counts_all %>% filter(lfc!=-1) %>% #, group!='Others') %>% 
                 left_join(tot_counts, by='group') %>% mutate('perc'=round(100*as.numeric(n)/tot,2))


# Plot change of number of genes
ggplotly(lfc_counts_all %>% filter(group != 'All') %>% 
         mutate(group = factor(group, levels = c('SFARI', 'Neuronal', 'Others'))) %>%
         ggplot(aes(lfc, perc, color=group)) + geom_point(aes(id=n)) + geom_line() +
         scale_color_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) + 
         ylab('Percentage of DE Genes') +  xlab('LFC threshold') + labs(color = 'Group') + 
         #ggtitle('Effect of filtering thresholds in SFARI Genes') + 
         theme_minimal() + theme(legend.position = 'bottom'))

rm(lfc_list, all_counts, Others_counts, Neuronal_counts, lfc_counts, lfc_counts_all, DE_genes, tot_counts)

---

2.4.2 Analysis of SFARI Genes by SFARI Score

Mean Expression

plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>% left_join(genes_info, by='ID')

wt = plot_data %>% wilcox_test(MeanExpr~gene.score, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1
base = 15.5
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
plot_data %>% ggplot(aes(gene.score, MeanExpr)) + 
              geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
              stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .01) +
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) + 
              xlab('') + ylab('Mean Expression') + 
              scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3', 
                                        'Others' = 'Other\ngenes', 'Neuronal' = 'Neuronal\ngenes')) +
              theme_minimal() + theme(legend.position='none')

Log Fold Change

genes_info %>% mutate(direction = ifelse(log2FoldChange>0, 'overexpressed', 'underexpressed')) %>% 
               group_by(gene.score, direction) %>% tally(name = 'overexpressed') %>% 
               filter(direction == 'overexpressed') %>% ungroup %>% 
               left_join(genes_info %>% group_by(gene.score) %>% tally(name = 'Total'), by = 'gene.score') %>% ungroup %>%
               mutate('underexpressed' = Total - overexpressed , 
                      'ratio' = round(overexpressed/underexpressed,2)) %>% 
               dplyr::select(gene.score, overexpressed, underexpressed, ratio) %>% 
               kable %>% kable_styling(full_width = F)

gene.score	overexpressed	underexpressed	ratio
1	66	78	0.85
2	102	103	0.99
3	211	229	0.92
Neuronal	673	671	1.00
Others	7194	6790	1.06

plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>% 
            left_join(genes_info, by='ID')

wt = plot_data %>% filter(log2FoldChange>0) %>% 
     wilcox_test(MeanExpr~gene.score, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1
base = 15.5
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
p1 = plot_data %>% filter(log2FoldChange>0) %>% ggplot(aes(gene.score, MeanExpr)) +
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) + 
     stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
     scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) + 
     xlab('') + ylab('Mean Expression') + ggtitle('Overexpressed genes') +
     scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3', 
                                        'Others' = 'Other\ngenes', 'Neuronal' = 'Neuronal\ngenes')) +
     theme_minimal() + theme(legend.position='none')

wt = plot_data %>% filter(log2FoldChange<0) %>% 
     wilcox_test(MeanExpr~gene.score, p.adjust.method='BH') %>% add_x_position(x = 'group')
p2 = plot_data %>% filter(log2FoldChange<0) %>% ggplot(aes(gene.score, MeanExpr)) +
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) + 
     stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
      scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) + 
     xlab('') + ylab('Mean Expression') + ggtitle('Underexpressed genes') +
     scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3', 
                                        'Others' = 'Other\ngenes', 'Neuronal' = 'Neuronal\ngenes')) +
     theme_minimal() + theme(legend.position='none')

grid.arrange(p1, p2, nrow=1)

Both with the original LFC values as well as the shrunken ones, the difference in LFC magnitude between SFARI genes and Neuronal genes is statisically significant with a p-value lower than \(10^{-4}\).

wt = genes_info %>% mutate(abs_lfc = abs(log2FoldChange), 
                           Group = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>% 
     wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')

increase = 0.04
base = 0.45
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
p1 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
     ggplot(aes(gene.score, abs(log2FoldChange))) + 
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) + 
     stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .005) + 
     scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) + 
     coord_cartesian(ylim= c(0, max(pos_y_comparisons))) + 
     scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3', 
                               'Neuronal' = 'Neuronal\ngenes','Others' = 'Other\ngenes')) +
     xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none')


wt = genes_info %>% mutate(abs_lfc = abs(shrunken_log2FoldChange), 
                           Group = factor(gene.score, levels = c('1','2','3','Others', 'Neuronal'))) %>% 
     wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')

increase = 0.03
base = 0.35
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
p2 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
     ggplot(aes(gene.score, abs(shrunken_log2FoldChange))) + 
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) + 
     stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .01) + 
     scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) + 
     coord_cartesian(ylim= c(0, max(pos_y_comparisons))) + 
     scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3', 
                               'Neuronal' = 'Neuronal\ngenes', 'Others' = 'Other\ngenes')) +
     xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none')

grid.arrange(p1, p2, nrow = 1)

rm(increase, base, pos_y_comparisons, wt)

Full plots, without cropping out outliers

p1 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
     ggplot(aes(gene.score, abs(log2FoldChange))) + 
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) + 
     scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) + 
     scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3', 
                               'Neuronal' = 'Neuronal\ngenes','Others' = 'Other\ngenes')) +
     xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none')

p2 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
     ggplot(aes(gene.score, abs(shrunken_log2FoldChange))) + 
     geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) + 
     scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) + 
     scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3', 
                               'Neuronal' = 'Neuronal\ngenes', 'Others' = 'Other\ngenes')) +
     xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none')

subplot(ggplotly(p1), ggplotly(p2), nrows=1)

rm(increase, base, pos_y_comparisons, wt)

## Warning in rm(increase, base, pos_y_comparisons, wt): object 'increase' not
## found

## Warning in rm(increase, base, pos_y_comparisons, wt): object 'base' not found

## Warning in rm(increase, base, pos_y_comparisons, wt): object 'pos_y_comparisons'
## not found

## Warning in rm(increase, base, pos_y_comparisons, wt): object 'wt' not found

Differential Expression

lfc_list = seq(0, 0.3, 0.01)

all_counts = data.frame('group'='All', 'n'=as.character(nrow(genes_info)))
Others_counts = data.frame('group'='Others', n=as.character(sum(genes_info$gene.score == 'Others')))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(genes_info$gene.score == 'Neuronal')))
lfc_counts_all = genes_info %>% group_by(`gene-score`) %>% filter(`gene-score` != 'Others') %>% tally %>%
                 mutate('group'=as.factor(`gene-score`), 'n'=as.character(n)) %>%
                 dplyr::select(group, n) %>%
                 bind_rows(Neuronal_counts, Others_counts, all_counts) %>%
                 mutate('lfc'=-1) %>%  dplyr::select(lfc, group, n)

for(lfc in lfc_list){
  
  # Recalculate genes_info with the new threshold (p-values change)
  DE_genes = results(dds, lfcThreshold=lfc, altHypothesis='greaterAbs') %>% data.frame %>%
             mutate('ID'=rownames(.)) %>% 
             left_join(genes_info %>% dplyr::select(ID, Neuronal, gene.score), by='ID') %>% 
             filter(padj<0.05 & abs(log2FoldChange)>lfc)

  
  # Calculate counts by groups
  all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_genes)))
  Others_counts = data.frame('group'='Others', n=as.character(sum(DE_genes$gene.score == 'Others')))
  Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_genes$gene.score == 'Neuronal')))
  lfc_counts = DE_genes %>% group_by(gene.score) %>% tally %>%
               mutate('group'=gene.score, 'n'=as.character(n)) %>%
               bind_rows(Neuronal_counts, all_counts) %>%
               mutate('lfc'=lfc) %>% dplyr::select(lfc, group, n)
  
  
  # Update lfc_counts_all
  lfc_counts_all = lfc_counts_all %>% bind_rows(lfc_counts)
}

# Add missing entries with 0s
lfc_counts_all = expand.grid('group'=unique(lfc_counts_all$group), 'lfc'=unique(lfc_counts_all$lfc)) %>% 
  left_join(lfc_counts_all, by=c('group','lfc')) %>% replace(is.na(.), 0)

# Calculate percentage of each group remaining
tot_counts = genes_info %>% group_by(`gene-score`) %>% tally() %>% filter(`gene-score`!='Others') %>%
             mutate('group'=factor(`gene-score`), 'tot'=n) %>% dplyr::select(group, tot) %>%
             bind_rows(data.frame('group'='Neuronal', 'tot'=sum(genes_info$gene.score == 'Neuronal')),
                       data.frame('group'='Others', 'tot'=sum(genes_info$gene.score == 'Others')),
                       data.frame('group'='All', 'tot'=nrow(genes_info)))

lfc_counts_all = lfc_counts_all %>% filter(lfc!=-1) %>% #, group!='Others') %>% 
                 left_join(tot_counts, by='group') %>% mutate('perc'=round(100*as.numeric(n)/tot,2))


# Plot change of number of genes
ggplotly(lfc_counts_all %>% filter(group != 'All') %>% 
         mutate(group = ifelse(group %in% c('1','2','3'), paste0('Score ',group), group)) %>% 
         mutate(group = factor(group, levels = c('Score 1', 'Score 2', 'Score 3', 'Neuronal', 'Others'))) %>%
         ggplot(aes(lfc, perc, color=group)) + 
         geom_point(aes(id=n)) + geom_line() + scale_color_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) + 
         ylab('Percentage of DE Genes') +  xlab('LFC threshold') +  labs(color = 'Group') +
         #ggtitle('Effect of filtering thresholds by SFARI score') + 
         #guides(color=guide_legend(nrow=2,byrow=TRUE) + # for breaking the legend into two rows
         theme_minimal() + theme(legend.position = 'bottom'))

rm(lfc_list, all_counts, Neuronal_counts, lfc_counts_all, lfc, lfc_counts, lfc_counts_all, tot_counts,
   lfc_counts_all, Others_counts)

---

Session info

sessionInfo()

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] kableExtra_1.1.0            knitr_1.32                 
##  [3] DESeq2_1.24.0               SummarizedExperiment_1.14.1
##  [5] DelayedArray_0.10.0         BiocParallel_1.18.1        
##  [7] matrixStats_0.56.0          Biobase_2.44.0             
##  [9] GenomicRanges_1.36.1        GenomeInfoDb_1.20.0        
## [11] IRanges_2.18.3              S4Vectors_0.22.1           
## [13] BiocGenerics_0.30.0         expss_0.10.2               
## [15] rstatix_0.6.0               ggpubr_0.2.5               
## [17] magrittr_2.0.1              GGally_1.5.0               
## [19] gridExtra_2.3               viridis_0.5.1              
## [21] viridisLite_0.4.0           RColorBrewer_1.1-2         
## [23] plotly_4.9.2                glue_1.4.2                 
## [25] reshape2_1.4.4              forcats_0.5.0              
## [27] stringr_1.4.0               dplyr_1.0.1                
## [29] purrr_0.3.4                 readr_1.3.1                
## [31] tidyr_1.1.0                 tibble_3.1.2               
## [33] ggplot2_3.3.5               tidyverse_1.3.0            
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1           backports_1.1.8        Hmisc_4.4-0           
##   [4] plyr_1.8.6             lazyeval_0.2.2         splines_3.6.3         
##   [7] crosstalk_1.1.0.1      digest_0.6.27          htmltools_0.5.1.1     
##  [10] fansi_0.5.0            checkmate_2.0.0        memoise_1.1.0         
##  [13] cluster_2.1.0          openxlsx_4.1.4         annotate_1.62.0       
##  [16] modelr_0.1.6           jpeg_0.1-8.1           colorspace_2.0-2      
##  [19] blob_1.2.1             rvest_0.3.5            haven_2.2.0           
##  [22] xfun_0.22              crayon_1.4.1           RCurl_1.98-1.2        
##  [25] jsonlite_1.7.2         genefilter_1.66.0      survival_3.2-7        
##  [28] gtable_0.3.0           zlibbioc_1.30.0        XVector_0.24.0        
##  [31] webshot_0.5.2          car_3.0-7              abind_1.4-5           
##  [34] scales_1.1.1           DBI_1.1.0              Rcpp_1.0.6            
##  [37] xtable_1.8-4           htmlTable_1.13.3       foreign_0.8-76        
##  [40] bit_4.0.4              Formula_1.2-3          htmlwidgets_1.5.1     
##  [43] httr_1.4.2             acepack_1.4.1          ellipsis_0.3.2        
##  [46] pkgconfig_2.0.3        reshape_0.8.8          XML_3.99-0.3          
##  [49] farver_2.1.0           nnet_7.3-14            sass_0.4.0            
##  [52] dbplyr_1.4.2           locfit_1.5-9.4         utf8_1.2.1            
##  [55] tidyselect_1.1.0       labeling_0.4.2         rlang_0.4.11          
##  [58] AnnotationDbi_1.46.1   munsell_0.5.0          cellranger_1.1.0      
##  [61] tools_3.6.3            cli_2.5.0              generics_0.0.2        
##  [64] RSQLite_2.2.0          broom_0.7.0            evaluate_0.14         
##  [67] yaml_2.2.1             bit64_4.0.5            fs_1.5.0              
##  [70] zip_2.0.4              xml2_1.2.5             compiler_3.6.3        
##  [73] rstudioapi_0.11        curl_4.3               png_0.1-7             
##  [76] ggsignif_0.6.0         reprex_0.3.0           geneplotter_1.62.0    
##  [79] bslib_0.2.5.1          stringi_1.5.3          highr_0.8             
##  [82] lattice_0.20-41        Matrix_1.2-18          vctrs_0.3.8           
##  [85] pillar_1.6.1           lifecycle_1.0.0        jquerylib_0.1.4       
##  [88] data.table_1.12.8      bitops_1.0-6           R6_2.5.0              
##  [91] latticeExtra_0.6-29    rio_0.5.16             assertthat_0.2.1      
##  [94] withr_2.4.2            GenomeInfoDbData_1.2.1 hms_0.5.3             
##  [97] grid_3.6.3             rpart_4.1-15           rmarkdown_2.7         
## [100] carData_3.0-3          lubridate_1.7.4        base64enc_0.1-3